
pip install pandas

Requirement already satisfied: pandas in c:\users\dell\anaconda3\lib\site-packages (1.1.3)Note: you may need to restart the kernel to use updated packages.

Requirement already satisfied: pytz>=2017.2 in c:\users\dell\anaconda3\lib\site-packages (from pandas) (2020.1)
Requirement already satisfied: numpy>=1.15.4 in c:\users\dell\anaconda3\lib\site-packages (from pandas) (1.19.2)
Requirement already satisfied: python-dateutil>=2.7.3 in c:\users\dell\anaconda3\lib\site-packages (from pandas) (2.8.1)
Requirement already satisfied: six>=1.5 in c:\users\dell\anaconda3\lib\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

import warnings
warnings.filterwarnings("ignore")
%matplotlib inline


df = pd.read_csv("Desktop\Data Science\Dataset_.csv")
print(df)

      Restaurant ID           Restaurant Name  Country Code              City  \
0           6317637          Le Petit Souffle           162       Makati City   
1           6304287          Izakaya Kikufuji           162       Makati City   
2           6300002    Heat - Edsa Shangri-La           162  Mandaluyong City   
3           6318506                      Ooma           162  Mandaluyong City   
4           6314302               Sambo Kojin           162  Mandaluyong City   
...             ...                       ...           ...               ...   
9546        5915730               Naml۱ Gurme           208         ��stanbul   
9547        5908749              Ceviz A��ac۱           208         ��stanbul   
9548        5915807                     Huqqa           208         ��stanbul   
9549        5916112               A���k Kahve           208         ��stanbul   
9550        5927402  Walter's Coffee Roastery           208         ��stanbul   

                                                Address  \
0     Third Floor, Century City Mall, Kalayaan Avenu...   
1     Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   
2     Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   
3     Third Floor, Mega Fashion Hall, SM Megamall, O...   
4     Third Floor, Mega Atrium, SM Megamall, Ortigas...   
...                                                 ...   
9546  Kemanke�� Karamustafa Pa��a Mahallesi, R۱ht۱m ...   
9547  Ko��uyolu Mahallesi, Muhittin ��st�_nda�� Cadd...   
9548  Kuru�_e��me Mahallesi, Muallim Naci Caddesi, N...   
9549  Kuru�_e��me Mahallesi, Muallim Naci Caddesi, N...   
9550  Cafea��a Mahallesi, Bademalt۱ Sokak, No 21/B, ...   

                                        Locality  \
0      Century City Mall, Poblacion, Makati City   
1     Little Tokyo, Legaspi Village, Makati City   
2     Edsa Shangri-La, Ortigas, Mandaluyong City   
3         SM Megamall, Ortigas, Mandaluyong City   
4         SM Megamall, Ortigas, Mandaluyong City   
...                                          ...   
9546                                    Karak�_y   
9547                                   Ko��uyolu   
9548                                 Kuru�_e��me   
9549                                 Kuru�_e��me   
9550                                        Moda   

                                       Locality Verbose   Longitude  \
0     Century City Mall, Poblacion, Makati City, Mak...  121.027535   
1     Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101   
2     Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831   
3     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475   
4     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508   
...                                                 ...         ...   
9546                                Karak�_y, ��stanbul   28.977392   
9547                               Ko��uyolu, ��stanbul   29.041297   
9548                             Kuru�_e��me, ��stanbul   29.034640   
9549                             Kuru�_e��me, ��stanbul   29.036019   
9550                                    Moda, ��stanbul   29.026016   

       Latitude                          Cuisines  ...          Currency  \
0     14.565443        French, Japanese, Desserts  ...  Botswana Pula(P)   
1     14.553708                          Japanese  ...  Botswana Pula(P)   
2     14.581404  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)   
3     14.585318                   Japanese, Sushi  ...  Botswana Pula(P)   
4     14.584450                  Japanese, Korean  ...  Botswana Pula(P)   
...         ...                               ...  ...               ...   
9546  41.022793                           Turkish  ...  Turkish Lira(TL)   
9547  41.009847   World Cuisine, Patisserie, Cafe  ...  Turkish Lira(TL)   
9548  41.055817            Italian, World Cuisine  ...  Turkish Lira(TL)   
9549  41.057979                   Restaurant Cafe  ...  Turkish Lira(TL)   
9550  40.984776                              Cafe  ...  Turkish Lira(TL)   

     Has Table booking Has Online delivery Is delivering now  \
0                  Yes                  No                No   
1                  Yes                  No                No   
2                  Yes                  No                No   
3                   No                  No                No   
4                  Yes                  No                No   
...                ...                 ...               ...   
9546                No                  No                No   
9547                No                  No                No   
9548                No                  No                No   
9549                No                  No                No   
9550                No                  No                No   

     Switch to order menu Price range  Aggregate rating  Rating color  \
0                      No           3               4.8    Dark Green   
1                      No           3               4.5    Dark Green   
2                      No           4               4.4         Green   
3                      No           4               4.9    Dark Green   
4                      No           4               4.8    Dark Green   
...                   ...         ...               ...           ...   
9546                   No           3               4.1         Green   
9547                   No           3               4.2         Green   
9548                   No           4               3.7        Yellow   
9549                   No           4               4.0         Green   
9550                   No           2               4.0         Green   

     Rating text Votes  
0      Excellent   314  
1      Excellent   591  
2      Very Good   270  
3      Excellent   365  
4      Excellent   229  
...          ...   ...  
9546   Very Good   788  
9547   Very Good  1034  
9548        Good   661  
9549   Very Good   901  
9550   Very Good   591  

[9551 rows x 21 columns]


display(df)


df.describe()


df.head()


df.tail()


df.columns

Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',
       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',
       'Average Cost for two', 'Currency', 'Has Table booking',
       'Has Online delivery', 'Is delivering now', 'Switch to order menu',
       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',
       'Votes'],
      dtype='object')


#Explore the dataset and identify the number of rows and columns.


df.shape

(9551, 21)


rows = df.shape[0] 
columns = df.shape[1]


print("Rows: " + str(rows)) 
print("Columns: " + str(columns))

Rows: 9551
Columns: 21


# Check for missing values in each column and handle them accordingly.


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9551 entries, 0 to 9550
Data columns (total 21 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Restaurant ID         9551 non-null   int64  
 1   Restaurant Name       9551 non-null   object 
 2   Country Code          9551 non-null   int64  
 3   City                  9551 non-null   object 
 4   Address               9551 non-null   object 
 5   Locality              9551 non-null   object 
 6   Locality Verbose      9551 non-null   object 
 7   Longitude             9551 non-null   float64
 8   Latitude              9551 non-null   float64
 9   Cuisines              9542 non-null   object 
 10  Average Cost for two  9551 non-null   int64  
 11  Currency              9551 non-null   object 
 12  Has Table booking     9551 non-null   object 
 13  Has Online delivery   9551 non-null   object 
 14  Is delivering now     9551 non-null   object 
 15  Switch to order menu  9551 non-null   object 
 16  Price range           9551 non-null   int64  
 17  Aggregate rating      9551 non-null   float64
 18  Rating color          9551 non-null   object 
 19  Rating text           9551 non-null   object 
 20  Votes                 9551 non-null   int64  
dtypes: float64(3), int64(5), object(13)
memory usage: 1.5+ MB


df['Cuisines'].value_counts()

North Indian                                              936
North Indian, Chinese                                     511
Chinese                                                   354
Fast Food                                                 354
North Indian, Mughlai                                     334
                                                         ... 
Desserts, American                                          1
North Indian, South Indian, Chinese, Mithai, Fast Food      1
Chinese, Thai, Asian, Japanese, Korean, Seafood             1
Irish                                                       1
Italian, Continental, Mexican, Lebanese                     1
Name: Cuisines, Length: 1825, dtype: int64


df = df.dropna(axis=0)


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 9542 entries, 0 to 9550
Data columns (total 21 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Restaurant ID         9542 non-null   int64  
 1   Restaurant Name       9542 non-null   object 
 2   Country Code          9542 non-null   int64  
 3   City                  9542 non-null   object 
 4   Address               9542 non-null   object 
 5   Locality              9542 non-null   object 
 6   Locality Verbose      9542 non-null   object 
 7   Longitude             9542 non-null   float64
 8   Latitude              9542 non-null   float64
 9   Cuisines              9542 non-null   object 
 10  Average Cost for two  9542 non-null   int64  
 11  Currency              9542 non-null   object 
 12  Has Table booking     9542 non-null   object 
 13  Has Online delivery   9542 non-null   object 
 14  Is delivering now     9542 non-null   object 
 15  Switch to order menu  9542 non-null   object 
 16  Price range           9542 non-null   int64  
 17  Aggregate rating      9542 non-null   float64
 18  Rating color          9542 non-null   object 
 19  Rating text           9542 non-null   object 
 20  Votes                 9542 non-null   int64  
dtypes: float64(3), int64(5), object(13)
memory usage: 1.6+ MB


df.shape

(9542, 21)


# Perform data type conversion if necessary.


df.dtypes

Restaurant ID             int64
Restaurant Name          object
Country Code              int64
City                     object
Address                  object
Locality                 object
Locality Verbose         object
Longitude               float64
Latitude                float64
Cuisines                 object
Average Cost for two      int64
Currency                 object
Has Table booking        object
Has Online delivery      object
Is delivering now        object
Switch to order menu     object
Price range               int64
Aggregate rating        float64
Rating color             object
Rating text              object
Votes                     int64
dtype: object


df.iloc[0]

Restaurant ID                                                     6317637
Restaurant Name                                          Le Petit Souffle
Country Code                                                          162
City                                                          Makati City
Address                 Third Floor, Century City Mall, Kalayaan Avenu...
Locality                        Century City Mall, Poblacion, Makati City
Locality Verbose        Century City Mall, Poblacion, Makati City, Mak...
Longitude                                                         121.028
Latitude                                                          14.5654
Cuisines                                       French, Japanese, Desserts
Average Cost for two                                                 1100
Currency                                                 Botswana Pula(P)
Has Table booking                                                     Yes
Has Online delivery                                                    No
Is delivering now                                                      No
Switch to order menu                                                   No
Price range                                                             3
Aggregate rating                                                      4.8
Rating color                                                   Dark Green
Rating text                                                     Excellent
Votes                                                                 314
Name: 0, dtype: object


# Analyze the distribution of the target variable ("Aggregate rating") and identify any class imbalances.


sns.distplot(df['Aggregate rating'],bins=35,rug=False,color="r",kde=True)
plt.show()


plt.hist(df['Aggregate rating'], edgecolor='black', bins=30)
plt.show()


import math
from scipy.stats import shapiro


shapiro(df['Aggregate rating'])

ShapiroResult(statistic=0.7897467017173767, pvalue=0.0)


# Calculate basic statistical measures (mean, median, standard deviation, etc.) for numerical columns.


Numeric_Columns = df.select_dtypes(include='number')
Numeric_Columns.describe()


#Explore the distribution of categorical variables like "Country Code", "City" and "Cuisines".


categorical_columns = ['Country Code','City',"Cuisines"]

for category in categorical_columns:
    print('\nDistribution of ',category, ':')
    print(df[category].value_counts())
    
sns.displot(df, x="Country Code", binwidth=20)

Distribution of  Country Code :
1      8652
216     425
215      80
214      60
30       60
189      60
148      40
208      34
14       24
162      22
94       21
191      20
166      20
184      20
37        4
Name: Country Code, dtype: int64

Distribution of  City :
New Delhi     5473
Gurgaon       1118
Noida         1080
Faridabad      251
Ghaziabad       25
              ... 
Lincoln          1
Yorkton          1
Penola           1
Macedon          1
Beechworth       1
Name: City, Length: 140, dtype: int64

Distribution of  Cuisines :
North Indian                                              936
North Indian, Chinese                                     511
Chinese                                                   354
Fast Food                                                 354
North Indian, Mughlai                                     334
                                                         ... 
Desserts, American                                          1
North Indian, South Indian, Chinese, Mithai, Fast Food      1
Chinese, Thai, Asian, Japanese, Korean, Seafood             1
Irish                                                       1
Italian, Continental, Mexican, Lebanese                     1
Name: Cuisines, Length: 1825, dtype: int64

<seaborn.axisgrid.FacetGrid at 0x21bf17e1eb0>


# Identify the top cuisines and cities with the highest number of restaurants.


Top_cuisines = df["Cuisines"].value_counts().head(15)
print(Top_cuisines)

North Indian                      936
North Indian, Chinese             511
Chinese                           354
Fast Food                         354
North Indian, Mughlai             334
Cafe                              299
Bakery                            218
North Indian, Mughlai, Chinese    197
Bakery, Desserts                  170
Street Food                       149
Pizza, Fast Food                  131
Chinese, Fast Food                118
Mithai, Street Food               116
South Indian                      112
Bakery, Fast Food                 108
Name: Cuisines, dtype: int64


Top_cities = df["City"].value_counts().head(15)
print(Top_cities)

New Delhi         5473
Gurgaon           1118
Noida             1080
Faridabad          251
Ghaziabad           25
Ahmedabad           21
Bhubaneshwar        21
Guwahati            21
Lucknow             21
Amritsar            21
Rio de Janeiro      20
Goa                 20
Mangalore           20
Pretoria            20
London              20
Name: City, dtype: int64


#Visualize the locations of restaurants on a map using latitude and longitude information.


pip install folium

Requirement already satisfied: folium in c:\users\dell\anaconda3\lib\site-packages (0.17.0)
Requirement already satisfied: numpy in c:\users\dell\anaconda3\lib\site-packages (from folium) (1.19.2)
Requirement already satisfied: branca>=0.6.0 in c:\users\dell\anaconda3\lib\site-packages (from folium) (0.7.2)
Requirement already satisfied: xyzservices in c:\users\dell\anaconda3\lib\site-packages (from folium) (2024.4.0)
Requirement already satisfied: requests in c:\users\dell\anaconda3\lib\site-packages (from folium) (2.24.0)
Requirement already satisfied: jinja2>=2.9 in c:\users\dell\anaconda3\lib\site-packages (from folium) (2.11.2)
Requirement already satisfied: idna<3,>=2.5 in c:\users\dell\anaconda3\lib\site-packages (from requests->folium) (2.10)
Requirement already satisfied: chardet<4,>=3.0.2 in c:\users\dell\anaconda3\lib\site-packages (from requests->folium) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\users\dell\anaconda3\lib\site-packages (from requests->folium) (1.25.11)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\dell\anaconda3\lib\site-packages (from requests->folium) (2020.6.20)
Requirement already satisfied: MarkupSafe>=0.23 in c:\users\dell\anaconda3\lib\site-packages (from jinja2>=2.9->folium) (1.1.1)
Note: you may need to restart the kernel to use updated packages.


conda install folium -c conda-forge

Collecting package metadata (current_repodata.json): ...working... done
Note: you may need to restart the kernel to use updated packages.


==> WARNING: A newer version of conda exists. <==
  current version: 4.14.0
  latest version: 24.5.0

Please update conda by running

    $ conda update -n base -c conda-forge conda

Solving environment: ...working... done

# All requested packages already installed.

Retrieving notices: ...working... done


import folium

m = folium.Map(location=(45.5236, -122.6750))


# Calculate the map center
map_center = [df['Latitude'].mean(), df['Longitude'].mean()]  
Restaurent = folium.Map(location=map_center, zoom_start=10)
# adding marker for each restaurants
for index, row in df.iterrows():
    folium.Marker([row['Latitude'], row['Longitude']], popup=row['Restaurant Name']).add_to(Restaurent)


# Save the map as an HTML file
Restaurent.save('restaurant_map.html')  
Restaurent


# Analyze the distribution of restaurants across different cities or countries.


city_counts = df['City'].value_counts()

# Count the number of restaurants in each country
country_counts = df['Country Code'].value_counts()

# Plot distribution by city
plt.figure(figsize=(12, 6))
city_counts.head(20).plot(kind='bar')
plt.title('Restaurant Distribution by City')
plt.xlabel('City')
plt.ylabel('Number of Restaurants')
plt.show()

# Plot distribution by country
plt.figure(figsize=(12, 6))
country_counts.head(20).plot(kind='bar')
plt.title('Restaurant Distribution by Country')
plt.xlabel('Country')
plt.ylabel('Number of Restaurants')
plt.show()


# Determine if there is any correlation between the restaurant's location and its rating.


# correaltions between age and sex columns
c = np.corrcoef(df["Latitude"],df["Aggregate rating"])
print('Correlations between location and rating\n',c)

Correlations between location and rating
 [[1.00000000e+00 1.96569018e-04]
 [1.96569018e-04 1.00000000e+00]]


sns.heatmap(df[['Latitude','Longitude','Aggregate rating']].corr(),annot=True)

<AxesSubplot:>

	Restaurant ID	Country Code	Longitude	Latitude	Average Cost for two	Price range	Aggregate rating	Votes
count	9.551000e+03	9551.000000	9551.000000	9551.000000	9551.000000	9551.000000	9551.000000	9551.000000
mean	9.051128e+06	18.365616	64.126574	25.854381	1199.210763	1.804837	2.666370	156.909748
std	8.791521e+06	56.750546	41.467058	11.007935	16121.183073	0.905609	1.516378	430.169145
min	5.300000e+01	1.000000	-157.948486	-41.330428	0.000000	1.000000	0.000000	0.000000
25%	3.019625e+05	1.000000	77.081343	28.478713	250.000000	1.000000	2.500000	5.000000
50%	6.004089e+06	1.000000	77.191964	28.570469	400.000000	2.000000	3.200000	31.000000
75%	1.835229e+07	1.000000	77.282006	28.642758	700.000000	2.000000	3.700000	131.000000
max	1.850065e+07	216.000000	174.832089	55.976980	800000.000000	4.000000	4.900000	10934.000000

	Restaurant ID	Country Code	Longitude	Latitude	Average Cost for two	Price range	Aggregate rating	Votes
count	9.542000e+03	9542.000000	9542.000000	9542.000000	9542.000000	9542.000000	9542.000000	9542.000000
mean	9.043301e+06	18.179208	64.274997	25.848532	1200.326137	1.804968	2.665238	156.772060
std	8.791967e+06	56.451600	41.197602	11.010094	16128.743876	0.905563	1.516588	430.203324
min	5.300000e+01	1.000000	-157.948486	-41.330428	0.000000	1.000000	0.000000	0.000000
25%	3.019312e+05	1.000000	77.081565	28.478658	250.000000	1.000000	2.500000	5.000000
50%	6.002726e+06	1.000000	77.192031	28.570444	400.000000	2.000000	3.200000	31.000000
75%	1.835260e+07	1.000000	77.282043	28.642711	700.000000	2.000000	3.700000	130.000000
max	1.850065e+07	216.000000	174.832089	55.976980	800000.000000	4.000000	4.900000	10934.000000

	Restaurant ID	Restaurant Name	Country Code	City	Address	Locality	Locality Verbose	Longitude	Latitude	Cuisines	...	Currency	Has Table booking	Has Online delivery	Is delivering now	Switch to order menu	Price range	Aggregate rating	Rating color	Rating text	Votes
0	6317637	Le Petit Souffle	162	Makati City	Third Floor, Century City Mall, Kalayaan Avenu...	Century City Mall, Poblacion, Makati City	Century City Mall, Poblacion, Makati City, Mak...	121.027535	14.565443	French, Japanese, Desserts	...	Botswana Pula(P)	Yes	No	No	No	3	4.8	Dark Green	Excellent	314
1	6304287	Izakaya Kikufuji	162	Makati City	Little Tokyo, 2277 Chino Roces Avenue, Legaspi...	Little Tokyo, Legaspi Village, Makati City	Little Tokyo, Legaspi Village, Makati City, Ma...	121.014101	14.553708	Japanese	...	Botswana Pula(P)	Yes	No	No	No	3	4.5	Dark Green	Excellent	591
2	6300002	Heat - Edsa Shangri-La	162	Mandaluyong City	Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...	Edsa Shangri-La, Ortigas, Mandaluyong City	Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...	121.056831	14.581404	Seafood, Asian, Filipino, Indian	...	Botswana Pula(P)	Yes	No	No	No	4	4.4	Green	Very Good	270
3	6318506	Ooma	162	Mandaluyong City	Third Floor, Mega Fashion Hall, SM Megamall, O...	SM Megamall, Ortigas, Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City, Mandal...	121.056475	14.585318	Japanese, Sushi	...	Botswana Pula(P)	No	No	No	No	4	4.9	Dark Green	Excellent	365
4	6314302	Sambo Kojin	162	Mandaluyong City	Third Floor, Mega Atrium, SM Megamall, Ortigas...	SM Megamall, Ortigas, Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City, Mandal...	121.057508	14.584450	Japanese, Korean	...	Botswana Pula(P)	Yes	No	No	No	4	4.8	Dark Green	Excellent	229
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9546	5915730	Naml۱ Gurme	208	��stanbul	Kemanke�� Karamustafa Pa��a Mahallesi, R۱ht۱m ...	Karak�_y	Karak�_y, ��stanbul	28.977392	41.022793	Turkish	...	Turkish Lira(TL)	No	No	No	No	3	4.1	Green	Very Good	788
9547	5908749	Ceviz A��ac۱	208	��stanbul	Ko��uyolu Mahallesi, Muhittin ��st�_nda�� Cadd...	Ko��uyolu	Ko��uyolu, ��stanbul	29.041297	41.009847	World Cuisine, Patisserie, Cafe	...	Turkish Lira(TL)	No	No	No	No	3	4.2	Green	Very Good	1034
9548	5915807	Huqqa	208	��stanbul	Kuru�_e��me Mahallesi, Muallim Naci Caddesi, N...	Kuru�_e��me	Kuru�_e��me, ��stanbul	29.034640	41.055817	Italian, World Cuisine	...	Turkish Lira(TL)	No	No	No	No	4	3.7	Yellow	Good	661
9549	5916112	A��k Kahve	208	��stanbul	Kuru�_e��me Mahallesi, Muallim Naci Caddesi, N...	Kuru�_e��me	Kuru�_e��me, ��stanbul	29.036019	41.057979	Restaurant Cafe	...	Turkish Lira(TL)	No	No	No	No	4	4.0	Green	Very Good	901
9550	5927402	Walter's Coffee Roastery	208	��stanbul	Cafea��a Mahallesi, Bademalt۱ Sokak, No 21/B, ...	Moda	Moda, ��stanbul	29.026016	40.984776	Cafe	...	Turkish Lira(TL)	No	No	No	No	2	4.0	Green	Very Good	591